A Taxonomy of Prompt Modifiers for Text-To-Image Generation
Text-to-image generation has seen an explosion of interest since 2021. Today, beautiful and intriguing digital images and artworks can be synthesized from textual inputs ("prompts") with deep generative models. Online communities around text-to-image generation and AI generated art have quickly emerged. This paper identifies six types of prompt modifiers used by practitioners in the online community based on a 3-month ethnographic study. The novel taxonomy of prompt modifiers provides researchers a conceptual starting point for investigating the practice of text-to-image generation, but may also help practitioners of AI generated art improve their images. We further outline how prompt modifiers are applied in the practice of "prompt engineering." We discuss research opportunities of this novel creative practice in the field of Human-Computer Interaction (HCI). The paper concludes with a discussion of broader implications of prompt engineering from the perspective of Human-AI Interaction (HAI) in future applications beyond the use case of text-to-image generation and AI generated art.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationPrompt EngineeringText to Image GenerationText-to-Image GenerationSimilar Papers 제목 키워드 기반
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models
It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipula…
DiversityEthicsImage GenerationPrompt Engineering+2Prompt Stealing Attacks Against Text-to-Image Generation Models
Text-to-Image generation models have revolutionized the artwork design process and enabled anyone to create high-quality images by entering text descriptions called prompts. Creating a high-quality prompt that consists o…
Image GenerationText to Image GenerationText-to-Image GenerationCollaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation
The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models, a common issue encountered by users is t…
Image GenerationText GenerationText to Image GenerationText-to-Image GenerationLinguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
Text-conditioned image generation models often generate incorrect associations between entities and their visual attributes. This reflects an impaired mapping between linguistic binding of entities and modifiers in the p…
AttributeImage GenerationSentenceText to Image Generation+1PROMPTMINER: Black-Box Prompt Stealing against Text-to-Image Generative Models via Reinforcement Learning and Fuzz Optimization
Text-to-image (T2I) generative models such as Stable Diffusion and FLUX can synthesize realistic, high-quality images directly from textual prompts. The resulting image quality depends critically on well-crafted prompts …
Reinforcement Learning